Optimal Estimation of Rejection Thresholds for Topic Spotting

In many applications of topic spotting technology, especially those that require a human review of in-topic documents, a low false alarm rate is a key requirement. Topic spotting techniques typically include a rejection scheme to filter out off-topic documents. In this paper we present a robust methodology for rejecting off-topic messages that, in addition to modeling the topics of interest, uses a so-called alternate model for topics that are not included in the set of topics of interest. Specifically, we introduce two novel techniques for estimating topic-specific rejection thresholds - a parametric technique that can be viewed as transformation of topic-independent thresholds, and a nonparametric technique based on constrained optimization of false rejections subject to a pre-specified number of false acceptances. Our experiments on newsgroup messages demonstrate that when adequate training data is available topic-specific threshold estimation techniques can outperform topic-independent thresholds in terms of the ROC curve.